[1] "year" "Area" "Seas" "Bio_smry" "SpawnBio"
[6] "Recruit_0" "retainB_1" "retainN_1" "retainB_2" "retainN_2"
[11] "retainB_3" "retainN_3" "retainB_4" "retainN_4" "retainB_5"
[16] "retainN_5" "deadB_1" "deadN_1" "deadB_2" "deadN_2"
[21] "deadB_3" "deadN_3" "deadB_4" "deadN_4" "deadB_5"
[26] "deadN_5" "F_1" "F_2" "F_3" "F_4"
[31] "F_5" "SPRratio" "rec_dev" "raw_rec_dev" "model_run"
[36] "iteration" "scenario"
Comparing Future Red Tide Scenarios
For this test run, I took the SEDAR Red Grouper stock assessment and used SSMSE to introduce future red tide events in the OM and EM. There were 6 total scenarios:
- default: the base stock assessment with red tide events in 2005 and 2014.
- red tide random 10: I used a custom function to generate 10 random red tide events in the projected years.
- red tide random 25: I used a custom function to generate 25 random red tide events in the projected years.
- red tide regular 3: I added a red tide event every 3 years.
- red tide regular 5: I added a red tide event every 5 years.
- red tide regular 5 mortality 5: I added a red tide event every 5 years and set the OM mortality to 0.5.
Import the results and summary files from the cloud.
Reviewing time series plots
List of the things we can plot with the ts_plot_variable function:
Here are the basic plots:
Long-Term SSB and Violin plots
These plots are similar to Doering et al. 2023
This plot takes the longest to process. It generates a plot of the SSB variation by scenario. Then it creates a series of violin plots with the OM and MS for Long-term average catch, Long-term catch variability, Short-term average catch, and Long-term average SSB.
This plot is similar to the Long-term catch variability plot [2] in the previous chunk, but this produces a plot of the coefficient of variation instead of Catch (metric tons).
This just combines the previous plots into a nice 2 by 2 patch work for easier viewing.
Reviewing derived quantities
Some of the same plots above but derived. Below is a list of all the derived quantity variables:
[1] "Value.SSB" "Value.Recr" "Value.SPRratio"
[4] "Value.F" "Value.Bratio" "Value.ForeCatch"
[7] "Value.OFLCatch" "Value.ForeCatchret" "Value.lnSPB"
[10] "year" "model_run" "iteration"
[13] "scenario"
Term Plots
General Brainstorm:
Quantify how often the stock crashes from red tide events? Quantify the loss of catch from each red tide event, then the average loss.
[1] "year" "Area" "Seas" "Bio_smry" "SpawnBio"
[6] "Recruit_0" "retainB_1" "retainN_1" "retainB_2" "retainN_2"
[11] "retainB_3" "retainN_3" "retainB_4" "retainN_4" "retainB_5"
[16] "retainN_5" "deadB_1" "deadN_1" "deadB_2" "deadN_2"
[21] "deadB_3" "deadN_3" "deadB_4" "deadN_4" "deadB_5"
[26] "deadN_5" "F_1" "F_2" "F_3" "F_4"
[31] "F_5" "SPRratio" "rec_dev" "raw_rec_dev" "model_run"
[36] "iteration" "scenario"
More plots
If a red tide event happens in the last 3 years of the simulation, Stock Synthesis overestimates the mortality event by X.
Weird plots
Mean ratio plots
Terminal year plots
Only red tide years
B/W terminal year plots
All years.
Fishing terminal year plots
Fishing terminal year plots all years
Plots of all years instead of just red tide years
Terminal year with added mean line
Only years with red tide events
Fishing terminal year plots
I was redoing the mean lines and sds, and noticed that the EM:OM ratio would be NA if the EM F is zero (happens a lot in F_5). I added those NAs back in as zeros and it dramatically dampens the F_5 “spike”. So I filtered this data to only years where a red tide occurred to highlight that the spike in the terminal year trend is only relevant to red tide years.
New complex Ratio plots
All years, biomass ratios
I was redoing the mean lines and sds, and noticed that the EM:OM ratio would be NA if the EM F is zero (happens a lot in F_5). I added those NAs back in as zeros and it dramatically dampens the F_5 “spike”. So I filtered this data to only years where a red tide occurred to highlight that the spike in the terminal year trend is only relevant to red tide years.
Red tide years, biomass ratio
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Red tide years, biomass ratio, just Com and Rec Retained and Discards
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Exploring Recruitment
“rec_dev” “raw_rec_dev” “Recruit_0”
Recruitment ratios, all years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Terminal year recruitment ratios, all years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Terminal year recruitment ratios, red tide years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Terminal year rec_devs ratios, all years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Terminal year rec_devs ratios, red tide years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Terminal year SSB ratios, red tide years
This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.
Inspired by Wetzel and Punt et al. 2011
I am going to attempt to make their Relative Error Plots.
RE = (E - T) / T
Variables of interest:
F_5, Recruit_0,
Warning: Removed 28 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Warning: Removed 540 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Warning: Removed 547 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Then I may attempt to calculate RMSE
Exploring Indecies
I was able to put cpue from one scenario/iteration but it doesn’t get projected past 2017.